comparison scran-normalize.R @ 0:9e2b0debeaea draft default tip

"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_scran_normalize commit ddcf915dd9b690d7f3876e08b939adde36cbb8dd"

0:9e2b0debeaea

# load packages that are provided in the conda env
options( show.error.messages=F,
       error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
warnings()

library(optparse)
library(scran)

#Arguments
option_list = list(
  make_option(
    c("-d", "--data"),
    default = NA,
    type = 'character',
    help = "Input file that contains count values to transform"
  ),
  make_option(
    c("-s", "--sep"),
    default = '\t',
    type = 'character',
    help = "File separator [default : '%default' ]"
  ),
  make_option(
    "--cluster",
    default=FALSE,
    action="store_true",
    type = 'logical',
    help = "Whether to calculate the size factor per cluster or on all cell"
  ),
  make_option(
    c("-m", "--method"),
    default = 'hclust',
    type = 'character',
    help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]"
  ),
  make_option(
    "--size",
    default = 100,
    type = 'integer',
    help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]"
  ),
  make_option(
    c("-o", "--out"),
    default = "res.tab",
    type = 'character',
    help = "Output name [default : '%default' ]"
  )
)

opt = parse_args(OptionParser(option_list = option_list),
                 args = commandArgs(trailingOnly = TRUE))

if (opt$sep == "tab") {opt$sep = "\t"}

data = read.table(
  opt$data,
  check.names = FALSE,
  header = TRUE,
  row.names = 1,
  sep = opt$sep
)

## Import data as a SingleCellExperiment object
sce <- SingleCellExperiment(list(counts=as.matrix(data)))


if(opt$cluster){
  clusters <- quickCluster(sce, min.size = opt$size, method = opt$method)

  ## Compute sum factors
  sce <- computeSumFactors(sce, cluster = clusters)
} else {

  ## Compute sum factors
  sce <- computeSumFactors(sce)
}

sce <- normalize(sce)

logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits=5), check.names = F)


write.table(
  logcounts,
  opt$out,
  col.names = T,
  row.names = F,
  quote = F,
  sep = "\t"
)